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Screen shot #13 (Merely a picture to illustrate that our GUI is totally self-explanatory)

TK-MIPs underlying Software Architecture can be reconfigured to match the structure of your application.

The flowchart above provides insights into how TK-MIP may best be reconfigured for most efficient processing to accommodate specific application structural simplifications. For situations involving time stamps used in data logging, let the above defined symbol delta: Δ = t(j+1) - tj.

The best or clearest discussion of the proper sequence of discrete-time KF operations (to be implemented into software) that we have encountered to date is found on pages 234-236 of Brown, Robert Grover, Hwang, Patrick Y. C., Introduction to Random Signals and Applied Kalman Filtering, 2nd Edition, John Wiley & Sons, Inc., New York, 1983, 1992. They also discuss an alternate formulation for implementing a discrete-time KF into software in Section 6.2 on pages 259-261 that is also correct but looks a little different. [They also provide an excellent derivation and discussion of D. T. Magills earlier 1965 Multiple Model of Magill (MMM) bank-of-Kalman-Filters approach to adaptive filtering {also called the Multiple Model Estimation Algorithm (MMEA)} in Section 9.3 of this wonderfully clear and insightful book. 

Magill's 1965 approach predated the Interactive Multiple Model (IMM) of Prof. Bar-Shalom (UCONN) and Prof. X.-Rong Li but it did not possess their lucrative and useful additional "bells and whistles", namely, IMM is potentially more responsive to changes by being willing to continue entertaining the several alternative filter model candidates that were hypothesized and enunciated as candidate models by virtue of the presence of the "sojourn time" and the "finite state Markov chain transition probabilities" that keeps the several alternative candidate filter models viable and active. Prof. Bar-Shalom, himself has warned that some people don't like the answers from IMM because, instead of yielding "black or white" selections or decisions as to the appropriate underlying model by picking only one of the specified candidate models enunciated in the figure above, IMM instead yields "shades of gray" by deciding on a final result that blends aspects of several of the underlying candidate models together as one. This consequential outcome is satisfactory for some applications but disappointing for other applications, depending on the application.]

Kalman Filter Structure for handling the automatic processing of two different periodic measurement streams (of different periods) from different types of measurement structures and quality (demonstrated here using MatLab).

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